completion labo 6 - #1
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Labo 6 |
dpo
reviewed
Aug 18, 2025
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Status: |
dpo
reviewed
Aug 19, 2025
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| function projected_gradient_norm(x, gx, C) | ||
| y = copy(x) | ||
| projection!(y .= x .- gx, C) |
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Un peu risqué et perte de lisibilité ; en gros, ce n'est pas une bonne pratique.
| g_0 = grad(model, x0) | ||
| gx = copy(g_0) | ||
| g0norm = projected_gradient_norm(x0, g_0, C) | ||
| gnorm = projected_gradient_norm(x, gx, C) |
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| return GenericExecutionStats(model; status=status, solution = x, | ||
| objective = obj(model, x), | ||
| primal_feas = NaN, |
| return GenericExecutionStats(model; status=status, solution = x, | ||
| objective = obj(model, x), | ||
| primal_feas = NaN, | ||
| dual_feas = NaN, |
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| @info log_header([:iter, :nf, :primal, :nd, :Δ], | ||
| [Int, Int, Float64, String, Float64, Float64], | ||
| hdr_override=Dict(:nf => "#F", :primal => "f(x)", :nd => "‖d‖", :Δ => "‖Δf‖")) |
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Ce serait important d'afficher gnorm puisque c'est la mesure de stationnarité ...
| iter_limit = iter > max_iter | ||
| tired = many_evals || el_time > max_time || iter_limit | ||
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| @info log_row(Any[iter, neval_obj(model), fvalue, pnorm, gnorm]) |
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Tu affiches gnorm dans une colonne qui s'appelle ∆f ...
| end | ||
| ``` | ||
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| Dans les trois cas, on arrive à convergé mais la solution n'est pas aussi bonne que Ipopt de plusieurs degrés de magnitures. |
Contributor
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Ce n'est pas très facile de comparer les solutions dans la sortie de ton code.
| x .= center .+ v .* (r / nv) | ||
| return x | ||
| end | ||
| end |
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| # Votre solution pourrait-elle être utilisée pour calculer un pas dans une méthode de région de confiance ? | ||
| Oui en fait, elle retourne exactement s. Le problème de cette méthode c'est qu'elle n'est pas optimale en ce sens où elle aura de la | ||
| difficulté à prendre une direction optimale lorsque le problème n'est pas convexe. Ainsi, la méthode peut être lente. |
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